基于前向-后向自校正扩散引导特征重建的图像融合
作者:
作者单位:

1.上海电力大学;2.上海交通大学

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家重点基础研究发展计划(973计划),国家自然科学基金项目(面上项目,重点项目,重大项目)


A Image Fusion Method Using Forward-Backward Self-correcting Diffusion Guided Feature Reconstruction
Author:
Affiliation:

1.Shanghai Univeity of Electric Power;2.Shanghai Jiao Tong University

Fund Project:

The National Basic Research Program of China (973 Program),The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    现有图像融合方法不同程度的存在边缘阶梯效应,会导致一些空间伪影引入融合图像。 本文提出了一种新的解决图像融合过程中鲁棒性差的方法,即前向-后向自校正扩散引导特征重建(Forward-backward Self-correcting diffusion,FBSD)。 算法充分考虑特征间过渡的梯度方差特性,根据扩散系数的阈值来抑制像素各方向的“热传递”。 针对后向扩散带来边缘锐化的问题,我们引入可变指数的分解方式,对扩散方向加以控制, 将后向扩散限制在一个有限的范围内,从而有效防止阶梯效应的形成。 并对分解后各特征之间的差异设计了 一种基于期望值最大算法和主成分分析的混杂融合策略。 最后利用评价指标评估了所提出算法的性能,验证 了该方法在边缘阶梯效应的处理上优于现有的图像融合方法,以及融合决策的有效性。

    Abstract:

    The existing image fusion methods have edge ladder effect in varying degrees, which will lead to some spatial artifacts introduced into the fused image. This paper proposes a new method to solve the poor robustness in the process of image fusion, that is, forward-backward Self-correcting diffusion guided feature reconstruction (Forward-backward Self-correcting diffusion, FBSD). The algorithm fully considers the gradient variance of the transition between features, and suppresses the ”heat transfer” of pixels in all directions according to the threshold of diffusion coefficient. In order to solve the problem of edge sharpening caused by backward diffusion, we introduce the decomposition method of variable index to control the diffusion direction and limit the backward diffusion to a limited range, so as to effectively prevent the formation of ladder effect. According to the differences between features after decomposition, a hybrid fusion strategy based on Expectation-Maximization algorithm and principal component analysis algorithm is designed. Finally, the evaluation index is used to evaluate the performance of the proposed algorithm, and it is verified that this method is better than the existing image fusion methods in dealing with the edge step effect, and the effectiveness of the fusion decision.

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  • 收稿日期:2021-02-02
  • 最后修改日期:2021-04-15
  • 录用日期:2021-04-21
  • 在线发布日期: 2021-07-01
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